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Single-cell multiomic analysis of mesenchymal cells reveals molecular signatures and regulators of lung allograft fibrosis
Lu Lu, A. Patrick McLinden, Natalie M. Walker, Ragini Vittal, Yichen Wang, Fatemeh Fattahi, Stephen T. Russell, Michael P. Combs, Joshua D. Welch, Vibha N. Lama
Lu Lu, A. Patrick McLinden, Natalie M. Walker, Ragini Vittal, Yichen Wang, Fatemeh Fattahi, Stephen T. Russell, Michael P. Combs, Joshua D. Welch, Vibha N. Lama
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Research Article Genetics Pulmonology

Single-cell multiomic analysis of mesenchymal cells reveals molecular signatures and regulators of lung allograft fibrosis

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Abstract

Survival after lung transplantation is limited by chronic, progressive graft failure, termed chronic lung allograft dysfunction (CLAD). Graft-resident mesenchymal cells (MCs) drive CLAD pathogenesis and exhibit stable dysregulated signaling, yet the transcriptomic and epigenomic drivers underlying this fibrogenic transformation remain elusive. We used single-cell multiomic profiling to characterize gene expression and chromatin accessibility in MCs isolated from bronchoalveolar lavage fluid of lung transplant recipients with and without CLAD, collected early after transplantation or after disease onset. MCs obtained after CLAD onset demonstrated a distinct transcriptomic signature compared with non-CLAD controls, enabling classification of disease status at the single-cell level with greater than 98% accuracy using signature genes. Chromatin accessibility analyses identified enrichment of CCAAT-enhancer-binding protein family transcription factors, specifically CEBPD, in CLAD MCs. MCs early after transplantation showed minimal accessibility differences, suggesting that CEBPD-associated regulatory changes emerge over time. Integration analyses identified 8 MC states and a CLAD-specific shift toward a fibrotic state. CEBPD, SOX4, and FOXP2 were identified as putative regulators of this state with substantial overlap in predicted targets. Targeting CEBPD reversed fibrotic phenotypes of CLAD MCs (decreased ECM expression, contractility, proliferation, and migration). Together, these data provide insights into transcriptomic and epigenomic changes in posttransplant MCs, facilitating the nomination of biomarkers and therapeutic targets.

Authors

Lu Lu, A. Patrick McLinden, Natalie M. Walker, Ragini Vittal, Yichen Wang, Fatemeh Fattahi, Stephen T. Russell, Michael P. Combs, Joshua D. Welch, Vibha N. Lama

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Figure 2

Classifying CLAD status from single-cell gene expression.

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Classifying CLAD status from single-cell gene expression.
(A) Workflow o...
(A) Workflow of training logistic regression classifier. (B) The blue line indicates average accuracy score of the logistic regression classifiers from 5-fold cross-validation. Error bars indicate the variance in accuracy across folds. The green lines indicate the number of genes with non-zero coefficients from the logistic regression classifiers trained on the test sets. (C) Heatmap showing the overlap of genes among different coefficients. (D) Heatmaps depicting the expression levels of gene signatures in CLAD patient–derived MCs constructed from the logistic regression classifiers with α of 0.1. Each column represents a cell, and each row represents 1 gene colored by gene expression level. (E) RT-qPCR analysis of CXCL12, DCN, CRLF1, and IL6ST expression from CLAD and non-CLAD cell lines. **P ≤ 0.01; ***P ≤ 0.001 by unpaired, 2-tailed Student’s t test.

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